Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare a safe, idempotent, closed-world read, so the safety burden is lifted. The description adds genuinely useful behavior beyond them: the null-vs-zero distinction ('a null coverage means that area has nothing to measure yet, which is different from a coverage of 0') tells the agent how to interpret empty results, which annotations cannot convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core definition before drilling into per-area meanings, and the trailing null-handling caveat is placed last where it belongs. It is one dense paragraph with an embedded list, which is slightly heavy but every clause (each coverage area plus the null rule) earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no parameters, no output schema, and full annotation coverage, the description carries the interpretive load well: it enumerates the likely return areas and, critically, explains how to read null results. Nothing an agent needs to call or interpret this tool is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so there is nothing for the description to disambiguate and the baseline is 4. The description correctly introduces no parameter discussion.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource and its exact semantics: per-area data coverage as a 0-1 share, with each area (margins, customers, catalogue/channels, documents) concretely defined. An agent can tell it apart from sibling getters like get_data_status or get_summary because the description pins down that it reports how much is known rather than the values themselves.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete trigger phrasings ('what do you know about us', 'how good is your data') and explicitly claims the COGS/cost-coverage question space. It stops short of naming when NOT to use it or pointing at a sibling alternative, which keeps it out of 5 territory.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.